Modern machine learning (ML) has seen tremendous success in various tasks across multiple domains
, surpassing human performance in many benchmarks [1]}, [2]}, [3]}. However, despite their impressive accuracies on test sets even after rigorous validation and testing, black-box deep learning models fail silently and catastrophically with highly confident predictions [4]}, [5]}, [6]}.
Such silent failures have severe consequences in mission-critical domains like healthcare and autonomous driving, where errors are costly, resulting in injury and death [7]}.
In the wake of changing data and concept drifts in the real world, where incoming samples can come from shifted distributions or completely new distributions [8]},
it is imperative that we continuously monitor models after deployment to assess when their predictions should not be trusted.
